Here, we present two structural function estimation experiments. One is a demand design experiment based on a synthetic dataset introduced by [1]}, which is a standard benchmark for the instrumental variable regression. Here, we modify the data generating process to provide a benchmark for PCL methods. We consider the problem of predicting sales \(Y\)  from ticket price \(P\) , where these are confounded by a potential demand \(D \in [0,10]\) . To correct this confounding bias, we use the fuel price \((C_1, C_2)\)  as the treatment-inducing proxy, which has an impact on price \(P\) , and the number of the views of the ticket reservation page \(V\)  as the outcome-inducing proxy. Details of the data generation process can be found in Appendix REF .
